WhatPulse Keypress Stats Analyzer¶

By Kenneth Burchfiel

Released under the MIT license

(I am not affiliated with WhatPulse (https://whatpulse.org) but highly recommend trying out the program, which I've used since September 2008. You can find my online WhatPulse page here: https://whatpulse.org/KBurchfiel)

This script allows you to perform various analyses of your WhatPulse typing stats. It does so by accessing the local WhatPulse database on your computer (along with other copies of databases you might want to include); reading this data into Pandas DataFrames, and then summarizing and graphing that data. The output of the script includes:

  1. Rolling average calculations at the 7-, 28-, and 365-day level
  2. Percentile and rank calculations
  3. Weekly and hourly keypress stats
  4. Static (.png) and interactive (.html) keypress charts

By converting this notebook into a Python file and then instructing your computer to run it on an hourly basis, you can also keep track of how much you're typing during your day. Which is sort of nerdy, to be honest. But so is this whole program ;)

I'll first import a number of packages that the program will use:

In [ ]:
import time
start_time = time.time() # Allows the program's runtime to be measured
import pandas as pd
import sqlalchemy
import numpy as np
from datetime import date
import statsmodels.api as sm
from scipy.stats import percentileofscore
import plotly.express as px
import kaleido
from IPython.display import Image
import datetime

I'll next define the names of folders that will store various outputs, along with default settings to use when converting interactive charts to static .png files:

In [ ]:
static_graphs_folder = 'graphs/static/'
interactive_graphs_folder = 'graphs/interactive/'
data_folder = 'data'

default_image_height = 540
default_aspect_ratio = 16 / 9 # Standard HD/UHD aspect ratio
default_image_width = default_image_height * default_aspect_ratio
default_image_scale = 5 # Creating a smaller graph (e.g. one 540 pixels 
# in height) and then scaling it helps keep the text a bit larger.

Importing Whatpulse data¶

In order to analyze my WhatPulse data, I'll first need to import it from my local Whatpulse SQLite database. I'll also import a copy of the Whatpulse SQLite database stored on my old laptop (so that my analysis doesn't have to be limited to my current computer's keypress data.)

You'll of course need to update the following cell with the path(s) to your own WhatPulse database(s).

Note: if you want to run this program on your own, but don't have access to a WhatPulse database, you can still run this program (assuming you've downloaded or cloned it from GitHub). Skip ahead to the line where I read in

In [ ]:
database_paths_list = ['C:/Users/kburc/AppData/Local/whatpulse/whatpulse.db', 
'C:/Users/kburc/D1V1/Documents/whatpulse_database_backups/a13r2_whatpulse.db',
'G:/My Drive/whatpulse_database_backups/linux_whatpulse.db'] 
# Note that the first path is to my computer's active database, 
# whereas additional paths point towards copies of databases from 
# other computers that I have used.

The following function analyzes each database's table at either the daily or hourly level.

In [ ]:
def generate_keypress_totals(database_path, level = 'daily'):
    '''The level argument can be 'daily', in which case the DataFrame
    returned by the function will show daily keypress totals, or 'hourly',
    which will preserve the hourly keypress totals in the original database.'''
    file_name = database_path.split('/')[-1] # Retrieves the final element
    # of the path (e.g. the file name)
    sqlalchemy_sqlite_engine = sqlalchemy.create_engine(
        'sqlite:///'+database_path) 
    # Based on https://docs.sqlalchemy.org/en/13/dialects/sqlite.html#connect-strings
    sqlalchemy_connection = sqlalchemy_sqlite_engine.connect()
    df_keypresses = pd.read_sql("select * from keypresses", 
    con = sqlalchemy_sqlite_engine) # Simply reads all of the data from this 
    # Capitalizing column names so that less renaming will be necessary
    # when creating graphs:
    df_keypresses.columns = [column.title() for column in df_keypresses.columns]

    # table into a Pandas DataFrame
    df_keypresses = df_keypresses.query("Day != '0000-00-00'").copy() # Removes
    # this blank date value from the database if it happens to be there
    if level == 'daily': # In this case, we'll want to combine hourly keypress
        # totals into a single row for each day
        df_keypresses = df_keypresses.pivot_table(
            index = 'Day', values = 'Count', aggfunc = 'sum')
        df_keypresses.sort_values('Day', inplace = True)
    elif level == 'hourly': # The original data is already displayed 
        # at the hourly level, so there's no need for a pivot_table() call.
        df_keypresses.sort_values(['Day', 'Hour'], inplace = True)
    else:
        raise ValueError("Unrecognized level argument passed to function.")
    df_keypresses.rename(columns={'Count':'Keypresses'},inplace=True)  
    return df_keypresses

I'll now run generate_keypress_totals in order to create a record of daily keypresses for both my current database and a copy of a past database. (I'll look at hourly keypress totals later on.)

In [ ]:
keypress_databases_list = []

for path in database_paths_list: # This loop creates a DataFrame for
    # each WhatPulse database stored in database_paths_list.
    # print("Now loading:",path)
    keypress_databases_list.append(generate_keypress_totals(
        path, level = 'daily'))

# I'll now combine these tables into a single DataFrame.
df_combined_daily_keypresses = pd.concat(
    [keypress_databases_list[i] for i in range(len(keypress_databases_list))])
df_combined_daily_keypresses.sort_index(inplace=True)

# At this point, my copy of df_combined_daily_keypresses has multiple
# entries for days in which I logged keys on multiple operating systems.
# Therefore, the following line groups these entries into a single row
# for each date.
df_combined_daily_keypresses = df_combined_daily_keypresses.reset_index(
).pivot_table(index = 'Day', values = 'Keypresses', aggfunc = 'sum')
df_combined_daily_keypresses.index = pd.to_datetime(
    df_combined_daily_keypresses.index)

df_combined_daily_keypresses.to_csv('data/df_combined_daily_keypresses.csv')
df_combined_daily_keypresses
Out[ ]:
Keypresses
Day
2020-09-21 19081
2020-09-22 32771
2020-09-23 32065
2020-09-24 34698
2020-09-25 47038
... ...
2023-06-13 23745
2023-06-14 50339
2023-06-15 45835
2023-06-16 40815
2023-06-17 12570

948 rows × 1 columns

The following line rebuilds df_combined_daily_keypresses using a copy of the DataFrame that got exported to a .csv file earlier on. This cell allows allow you to run this script even if you don't have your own WhatPulse database.

In [ ]:
df_combined_daily_keypresses = pd.read_csv(
    'data/df_combined_daily_keypresses.csv', index_col='Day')
# The following line makes the index compatible with
# date operations that the following code block will perform.
df_combined_daily_keypresses.index = pd.to_datetime(
df_combined_daily_keypresses.index)
df_combined_daily_keypresses
Out[ ]:
Keypresses
Day
2020-09-21 19081
2020-09-22 32771
2020-09-23 32065
2020-09-24 34698
2020-09-25 47038
... ...
2023-06-13 23745
2023-06-14 50339
2023-06-15 45835
2023-06-16 40815
2023-06-17 12570

948 rows × 1 columns

The following code block fills in the DataFrame with missing dates (e.g. dates in which I did not have any keypresses). I want to add in those missing dates so that I can calculate more accurate rolling keypress averages.

In [ ]:
first_date = df_combined_daily_keypresses.index[0]
last_date = df_combined_daily_keypresses.index[-1]
full_date_range = pd.date_range(start=first_date, end = last_date) 
# https://pandas.pydata.org/docs/reference/api/pandas.date_range.html
df_combined_daily_keypresses = df_combined_daily_keypresses.reindex(
    full_date_range, fill_value=0) 
# See https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.reindex.html

df_combined_daily_keypresses.index.name = 'Date'
df_combined_daily_keypresses.reset_index(inplace=True)

Now that I have a more complete daily keypress history, I can begin performing analyses on this data. First, I will use the rolling() function within Pandas to calculate 7-, 28-, and 365-day moving averages. Next, I will use the Series.rank() function twice to (1) calculate the percentile of each keypress and (2) determine each keypress's rank within the entire DataFrame.

In [ ]:
df_combined_daily_keypresses['7_day_ma'] = df_combined_daily_keypresses[
    'Keypresses'].rolling(7).mean()
df_combined_daily_keypresses['28_day_ma'] = df_combined_daily_keypresses[
    'Keypresses'].rolling(28).mean() 
# I switched from a 30-day MA to a 28-day MA because my average keypresses vary 
# significantly by weekday, meaning that a 30-day average would be skewed by 
# the number of Saturdays and Sundays present in the data.
df_combined_daily_keypresses['365_day_ma'] = df_combined_daily_keypresses[
    'Keypresses'].rolling(365).mean()

df_combined_daily_keypresses['percentile'] = 100*df_combined_daily_keypresses[
    'Keypresses'].rank(pct=True)
# The pct=True argument generates percentile values for each keypress value.
df_combined_daily_keypresses['rank'] = df_combined_daily_keypresses[
    'Keypresses'].rank(ascending = False)
# Ascending = False instructs the function to assign the lowest number 
# (e.g. 1) to the highest value.
# If two dates are tied, the rank may end in a 0.5. That's why these ranks 
# appear as floats but not integers. 
df_combined_daily_keypresses
Out[ ]:
Date Keypresses 7_day_ma 28_day_ma 365_day_ma percentile rank
0 2020-09-21 19081 NaN NaN NaN 40.30 598.0
1 2020-09-22 32771 NaN NaN NaN 67.50 326.0
2 2020-09-23 32065 NaN NaN NaN 65.90 342.0
3 2020-09-24 34698 NaN NaN NaN 70.55 295.5
4 2020-09-25 47038 NaN NaN NaN 88.50 116.0
... ... ... ... ... ... ... ...
995 2023-06-13 23745 18202.571429 24115.464286 20799.172603 48.90 512.0
996 2023-06-14 50339 21198.428571 24620.357143 20934.254795 91.80 83.0
997 2023-06-15 45835 23978.000000 25124.642857 21042.520548 87.40 127.0
998 2023-06-16 40815 29523.142857 25422.285714 21048.158904 80.70 194.0
999 2023-06-17 12570 31318.857143 25237.607143 21033.315068 29.10 710.0

1000 rows × 7 columns

Next, I'll add in weekdays using the Series.map() function within Pandas.

In [ ]:
weekday_dict = {0:'Monday',1:'Tuesday',2:'Wednesday',3:'Thursday',4:'Friday',
5:'Saturday',6:'Sunday'}
# weekday numbers in Python begin with 0 for Monday and end with 6 for Sunday. 
# See https://docs.python.org/3/library/datetime.html#datetime.date.weekday
weekday_dict
Out[ ]:
{0: 'Monday',
 1: 'Tuesday',
 2: 'Wednesday',
 3: 'Thursday',
 4: 'Friday',
 5: 'Saturday',
 6: 'Sunday'}

The following cell adds a 'Weekday' column to the DataFrame by (1) calculating the numerical weekday values for each date in the 'Date' column, then (2) using weekday_dict to map these numerical values to the weekday names.

In [ ]:
df_combined_daily_keypresses['Weekday'] = df_combined_daily_keypresses[
    'Date'].dt.weekday.map(weekday_dict)

Here are my daily keypress statistics for the last 10 days. Note the presence of the moving average, percentile, rank, and weekday columns.

In [ ]:
df_combined_daily_keypresses.tail(10)
Out[ ]:
Date Keypresses 7_day_ma 28_day_ma 365_day_ma percentile rank Weekday
990 2023-06-08 26378 26142.714286 24598.321429 20701.928767 54.70 454.0 Thursday
991 2023-06-09 1999 21335.428571 23868.785714 20635.723288 13.30 868.0 Friday
992 2023-06-10 0 20283.428571 23562.821429 20623.246575 2.85 972.5 Saturday
993 2023-06-11 14647 18180.142857 24085.928571 20663.087671 32.20 679.0 Sunday
994 2023-06-12 31281 18811.857143 24450.571429 20748.789041 64.20 359.0 Monday
995 2023-06-13 23745 18202.571429 24115.464286 20799.172603 48.90 512.0 Tuesday
996 2023-06-14 50339 21198.428571 24620.357143 20934.254795 91.80 83.0 Wednesday
997 2023-06-15 45835 23978.000000 25124.642857 21042.520548 87.40 127.0 Thursday
998 2023-06-16 40815 29523.142857 25422.285714 21048.158904 80.70 194.0 Friday
999 2023-06-17 12570 31318.857143 25237.607143 21033.315068 29.10 710.0 Saturday

Data Analysis¶

I'll start my data analysis by calculating some summary statistics. In the output below, 'count' shows the number of days since the earliest entry in my database, rather than the number of days for which I have keypress data (as the table also includes days without any keypresses).

In [ ]:
df_combined_daily_keypresses['Keypresses'].describe()
Out[ ]:
count     1000.000000
mean     24788.206000
std      17710.280994
min          0.000000
25%       9326.500000
50%      24362.000000
75%      36859.250000
max      90447.000000
Name: Keypresses, dtype: float64

Next, I'll calculate my top 50 daily keypress totals. Note that the top-ranked date has a rank of 1 and a percentile of 100.

In [ ]:
df_max_keypresses = df_combined_daily_keypresses.sort_values(
    'Keypresses', ascending = False).reset_index(drop=True)
df_max_keypresses.insert(0, 'Rank', df_max_keypresses.index+1)
keypress_difference_list = [
    df_max_keypresses.iloc[i]['Keypresses'] - df_max_keypresses.iloc[i+1][
        'Keypresses'] for i in range(len(df_max_keypresses) -1 )]
# The above list comprehension calculates the difference 
# between each row and the row below it. This isn't possible for the final row,
# so I'll instead append a np.Nan to it.)
keypress_difference_list.append(np.NaN)
df_max_keypresses['difference_from_lower_rank'] = keypress_difference_list
df_max_keypresses.head(50)
Out[ ]:
Rank Date Keypresses 7_day_ma 28_day_ma 365_day_ma percentile rank Weekday difference_from_lower_rank
0 1 2022-12-05 90447 34728.714286 26518.821429 20476.994521 100.0 1.0 Monday 12694.0
1 2 2021-02-22 77753 47983.000000 41390.857143 NaN 99.9 2.0 Monday 1440.0
2 3 2020-11-23 76313 50377.571429 39924.107143 NaN 99.8 3.0 Monday 869.0
3 4 2020-11-19 75444 47285.571429 37945.892857 NaN 99.7 4.0 Thursday 1656.0
4 5 2021-11-09 73788 37843.285714 30462.071429 29584.802740 99.6 5.0 Tuesday 878.0
5 6 2022-05-03 72910 30511.428571 28489.285714 24401.624658 99.5 6.0 Tuesday 2319.0
6 7 2021-02-08 70591 30177.428571 34437.357143 NaN 99.4 7.0 Monday 2278.0
7 8 2021-04-06 68313 40455.857143 40719.178571 NaN 99.3 8.0 Tuesday 383.0
8 9 2021-02-15 67930 46174.571429 40268.535714 NaN 99.2 9.0 Monday 397.0
9 10 2020-11-30 67533 20747.428571 36843.714286 NaN 99.1 10.0 Monday 28.0
10 11 2021-02-17 67505 45946.571429 40801.607143 NaN 99.0 11.0 Wednesday 988.0
11 12 2021-10-12 66517 39971.285714 36729.892857 29886.202740 98.9 12.0 Tuesday 582.0
12 13 2021-10-14 65935 46708.142857 37694.500000 30001.038356 98.8 13.0 Thursday 28.0
13 14 2021-09-23 65907 33891.142857 27215.107143 30006.980822 98.7 14.0 Thursday 443.0
14 15 2021-09-27 65464 35955.714286 29346.285714 29877.052055 98.6 15.0 Monday 561.0
15 16 2021-01-25 64903 43493.714286 27181.714286 NaN 98.5 16.0 Monday 30.0
16 17 2020-10-01 64873 41882.285714 NaN NaN 98.4 17.0 Thursday 190.0
17 18 2023-05-31 64683 26243.000000 21224.750000 20534.572603 98.3 18.0 Wednesday 1534.0
18 19 2022-08-22 63149 23130.428571 21345.785714 22784.887671 98.2 19.0 Monday 227.0
19 20 2021-10-26 62922 30820.000000 37036.107143 29899.287671 98.1 20.0 Tuesday 254.0
20 21 2021-04-07 62668 41278.000000 40999.857143 NaN 98.0 21.0 Wednesday 72.0
21 22 2022-04-26 62596 31803.714286 29291.607143 23983.284932 97.9 22.0 Tuesday 123.0
22 23 2021-02-19 62473 47705.571429 41579.714286 NaN 97.8 23.0 Friday 443.0
23 24 2022-05-26 62030 21439.571429 20150.964286 23948.786301 97.7 24.0 Thursday 61.0
24 25 2020-12-15 61969 44707.000000 35868.785714 NaN 97.6 25.0 Tuesday 532.0
25 26 2020-12-03 61437 36799.571429 36592.642857 NaN 97.5 26.0 Thursday 237.0
26 27 2022-04-25 61200 28113.428571 28472.321429 23845.347945 97.4 27.0 Monday 247.0
27 28 2022-01-21 60953 38414.857143 17726.821429 27924.994521 97.3 28.0 Friday 63.0
28 29 2020-10-08 60890 42957.571429 NaN NaN 97.2 29.0 Thursday 70.0
29 30 2021-09-22 60820 31844.285714 25305.500000 29914.263014 97.1 30.0 Wednesday 62.0
30 31 2021-03-25 60758 42687.000000 33748.321429 NaN 97.0 31.0 Thursday 50.0
31 32 2022-03-23 60708 18780.428571 12933.750000 24456.224658 96.9 32.0 Wednesday 561.0
32 33 2020-11-17 60147 42827.000000 36111.750000 NaN 96.8 33.0 Tuesday 414.0
33 34 2021-03-30 59733 44573.285714 36193.071429 NaN 96.7 34.0 Tuesday 222.0
34 35 2021-10-05 59511 41257.000000 35278.321429 29834.556164 96.6 35.0 Tuesday 553.0
35 36 2021-02-01 58958 41228.428571 35547.750000 NaN 96.5 36.0 Monday 86.0
36 37 2021-01-22 58872 32598.142857 23012.785714 NaN 96.4 37.0 Friday 108.0
37 38 2021-03-24 58764 37907.142857 32834.714286 NaN 96.3 38.0 Wednesday 511.0
38 39 2021-12-06 58253 28883.571429 27414.107143 28866.978082 96.2 39.0 Monday 63.0
39 40 2022-01-13 58190 24615.000000 10116.392857 27780.265753 96.1 40.0 Thursday 125.0
40 41 2021-10-13 58065 43126.857143 37181.785714 29952.904110 96.0 41.0 Wednesday 151.0
41 42 2022-10-13 57914 27980.285714 17756.071429 20765.580822 95.9 42.0 Thursday 524.0
42 43 2020-11-04 57390 28840.285714 33667.428571 NaN 95.8 43.0 Wednesday 10.0
43 44 2021-10-11 57380 38970.428571 36170.892857 29777.947945 95.7 44.0 Monday 52.0
44 45 2021-12-14 57328 24989.285714 25229.357143 28616.687671 95.6 45.0 Tuesday 415.0
45 46 2021-03-31 56913 44308.857143 38024.535714 NaN 95.5 46.0 Wednesday 36.0
46 47 2023-04-10 56877 27932.857143 28698.142857 21789.172603 95.4 47.0 Monday 85.0
47 48 2021-03-22 56792 37541.857143 31575.607143 NaN 95.3 48.0 Monday 338.0
48 49 2020-11-18 56454 44114.285714 36638.392857 NaN 95.2 49.0 Wednesday 112.0
49 50 2021-03-09 56342 22351.428571 35691.178571 NaN 95.1 50.0 Tuesday 17.0

Next, I'll create a visualization of these 50 dates. I will use Plotly instead of Matplotlib so that I can create both interactive (.html) and static (.png) versions of each chart. The static versions are easier to share, but the interactive versions are easier to analyze, as you can hover over the chart to get more information and zoom in on areas of particular interest.

First, I'll create a function that will make it easier to save .html and .png versions of these charts:

In [ ]:
def save_chart(fig, file_name, 
interactive_graphs_folder = interactive_graphs_folder, 
static_graphs_folder = static_graphs_folder):
    '''Saves a Plotly figure as a .HTML and .PNG file.
    fig: The Plotly figure to save.
    file_name: The filename to use. Don't include the extension.'''
    fig.write_html(
        interactive_graphs_folder+'/'+file_name+'.html')
    # Saving the interactive chart to a .png file:
    fig.write_image(
        static_graphs_folder+'/'+file_name+'.png',
        width = default_image_width, height = default_image_height,
        scale = default_image_scale)
In [ ]:
# Generating the interactive chart:
fig_max_keypresses = px.bar(df_max_keypresses.head(50), 
x = 'Rank', y = 'Keypresses', title = 'Top 50 Daily Keypress Totals', 
text = 'Keypresses')

save_chart(fig_max_keypresses, 'top_50_keypresses')

Here's a look at the chart:

Note: this and other .html-based charts will probably not display for you within GitHub. In order to view them, you will need to download the files from GitHub (e.g. by cloning them) and view them on your computer.

In [ ]:
fig_max_keypresses

Here's a copy of the static chart in case the .html chart above did not load for you: (I'll also display static versions of later charts as well.)

In [ ]:
Image(static_graphs_folder+'top_50_keypresses.png')
Out[ ]:

Keypress percentile data:¶

First, I'll calculate the keypress totals equal to the 90th through 100th percentiles (in 1-percentile increments).

In [ ]:
df_combined_daily_keypresses['Keypresses'].describe(
    percentiles=np.linspace(0.9,1,11))[5:-1]
# The first four rows and final row provide additional descriptive statistics,
# so we can get rid of them
# by adding [4:-1] to the end of this line.
# There is probably a more elegant solution that retrieves only percentile
# data, but this option is fairly straightforward.
Out[ ]:
90%     48630.60
91%     49495.37
92%     50553.12
93%     52561.22
94%     54465.96
95%     56325.85
96%     58070.00
97%     60759.86
98%     62673.08
99%     67505.28
100%    90447.00
Name: Keypresses, dtype: float64

Scipy's percentileofscore() function can be used to calculate the percentile corresponding to a specific keypress total. For instance, here's the percentile for a day with only a single keypress: (The percentile may be higher than you'd expect due to the presence of many days with 0 keypresses)

In [ ]:
percentileofscore(df_combined_daily_keypresses['Keypresses'], 1)
Out[ ]:
5.7

Next, I'll create a DataFrame showing the keypresses corresponding to every 5th percentile.

In [ ]:
df_percentiles = df_combined_daily_keypresses['Keypresses'].describe(
percentiles=np.linspace(0,1,21))[4:-1].reset_index().sort_values(
    'Keypresses', ascending = False).reset_index(drop=True).rename(
        columns={'index':'percentile'})
# Calling reset_index() twice appears inefficient, but it makes it easier
# to sort by a particular value (in this case, keypresses).
keypress_difference_list = [df_percentiles.iloc[
    i, 1] - df_percentiles.iloc[i+1, 1] for i in range(len(df_percentiles) -1 )]
# This list comprehension calculates the difference between each row and 
# the row below it. This isn't possible for the final row,
# so we'll instead append a np.Nan to it.)
keypress_difference_list.append(np.NaN)
df_percentiles['difference_from_lower_percentile'] = keypress_difference_list
df_percentiles
Out[ ]:
percentile Keypresses difference_from_lower_percentile
0 100% 90447.00 34121.15
1 95% 56325.85 7695.25
2 90% 48630.60 4560.00
3 85% 44070.60 3955.20
4 80% 40115.40 3256.15
5 75% 36859.25 2436.55
6 70% 34422.70 2805.85
7 65% 31616.85 2821.05
8 60% 28795.80 2252.40
9 55% 26543.40 2181.40
10 50% 24362.00 2931.20
11 45% 21430.80 2429.80
12 40% 19001.00 2576.45
13 35% 16424.55 3477.55
14 30% 12947.00 3620.50
15 25% 9326.50 3125.90
16 20% 6200.60 3438.65
17 15% 2761.95 1920.55
18 10% 841.40 841.40
19 5% 0.00 0.00
20 0% 0.00 NaN

The following chart shows the difference in keypresses between the different percentiles on this list.

In [ ]:
px.bar(df_percentiles, x = 'percentile', 
       y = 'difference_from_lower_percentile')

And here's a histogram that shows the frequency of different keypress ranges:

In [ ]:
fig_keypress_hist = px.histogram(df_combined_daily_keypresses, 
x = 'Keypresses', nbins = 40, text_auto = True, 
title = 'Histogram of Daily Keypress Totals')
# See https://plotly.com/python/histograms/
fig_keypress_hist.update_layout(bargap=0.1, yaxis_title = 'Days')
# https://plotly.com/python/histograms/
save_chart(fig_keypress_hist, 'daily_keypress_histogram')
fig_keypress_hist
In [ ]:
Image(static_graphs_folder+'daily_keypress_histogram.png')
Out[ ]:

I'll now create a pivot table that shows my average keypresses per weekday:

In [ ]:
df_weekday_pivot = df_combined_daily_keypresses.pivot_table(
    index = 'Weekday', values = 'Keypresses', aggfunc = 'mean').sort_values(
    'Weekday', ascending = False).reset_index()
# Adding in a Weekday_Order column so that 
# weekdays can be sorted chronologically:
df_weekday_pivot['Weekday_Order'] = df_weekday_pivot['Weekday'].map(
{'Sunday':0,'Monday':1,"Tuesday":2,"Wednesday":3,
"Thursday":4,"Friday":5,"Saturday":6})
df_weekday_pivot
Out[ ]:
Weekday Keypresses Weekday_Order
0 Wednesday 30126.776224 3
1 Tuesday 32195.909091 2
2 Thursday 30975.510490 4
3 Sunday 11266.450704 0
4 Saturday 13845.580420 6
5 Monday 31287.454545 1
6 Friday 23725.202797 5
In [ ]:
df_weekday_pivot.sort_values('Weekday_Order', inplace = True)
df_weekday_pivot
Out[ ]:
Weekday Keypresses Weekday_Order
3 Sunday 11266.450704 0
5 Monday 31287.454545 1
1 Tuesday 32195.909091 2
0 Wednesday 30126.776224 3
2 Thursday 30975.510490 4
6 Friday 23725.202797 5
4 Saturday 13845.580420 6

Graphing my average keypresses per weekday:

In [ ]:
fig_keypresses_by_weekday = px.bar(df_weekday_pivot, x = 'Weekday', 
y = 'Keypresses', text_auto = '.0f', color = 'Keypresses', 
color_continuous_scale = 'PrGn')
# For text_auto, see: 
# https://plotly.com/python-api-reference/generated/plotly.express.bar

# For color scale options and usage, 
# visit https://plotly.com/python/builtin-colorscales/
fig_keypresses_by_weekday.update_traces(marker_line_color='rgb(0,0,0)', 
                                        marker_line_width=0.5, opacity=1)
fig_keypresses_by_weekday

save_chart(fig_keypresses_by_weekday, file_name = 'keypresses_by_weekday')
In [ ]:
Image(static_graphs_folder+'keypresses_by_weekday.png')
Out[ ]:

Total keypresses since first date in DataFrame:¶

In [ ]:
print("Total keypresess since", str(
df_combined_daily_keypresses.iloc[0]['Date'])+":",'{:,}'.format(
sum(df_combined_daily_keypresses['Keypresses'])))

Total keypresess since 2020-09-21 00:00:00: 24,788,206

Keypresses over the past 50 days:¶

In [ ]:
df_combined_daily_keypresses.tail(50) # Last 50 days
Out[ ]:
Date Keypresses 7_day_ma 28_day_ma 365_day_ma percentile rank Weekday
950 2023-04-29 86 984.571429 10928.107143 20472.975342 6.60 935.0 Saturday
951 2023-04-30 728 1004.714286 10709.571429 20429.076712 9.70 904.0 Sunday
952 2023-05-01 19015 3704.428571 10657.785714 20459.320548 40.10 600.0 Monday
953 2023-05-02 34457 8225.714286 10572.857143 20446.194521 70.10 300.0 Tuesday
954 2023-05-03 18004 10750.142857 10278.821429 20295.767123 37.70 624.0 Wednesday
955 2023-05-04 37630 16069.000000 10907.857143 20328.997260 76.00 241.0 Thursday
956 2023-05-05 18761 18383.000000 11071.035714 20359.627397 39.40 607.0 Friday
957 2023-05-06 169 18394.857143 10309.500000 20360.090411 7.40 927.0 Saturday
958 2023-05-07 560 18370.857143 9619.500000 20359.041096 9.00 911.0 Sunday
959 2023-05-08 19163 18392.000000 8272.571429 20411.542466 40.40 597.0 Monday
960 2023-05-09 8680 14709.571429 7695.642857 20380.824658 24.30 758.0 Tuesday
961 2023-05-10 11651 13802.000000 7024.892857 20339.323288 27.90 722.0 Wednesday
962 2023-05-11 21576 11508.571429 7114.928571 20352.210959 45.40 547.0 Thursday
963 2023-05-12 22426 12032.142857 7915.857143 20365.186301 46.40 537.0 Friday
964 2023-05-13 8567 13231.857143 8221.464286 20261.473973 24.00 761.0 Saturday
965 2023-05-14 0 13151.857143 8221.464286 20147.783562 2.85 972.5 Sunday
966 2023-05-15 21071 13424.428571 8974.000000 20185.317808 44.20 559.0 Monday
967 2023-05-16 33128 16917.000000 10157.142857 20180.243836 67.80 323.0 Tuesday
968 2023-05-17 36202 20424.285714 11450.071429 20253.912329 73.50 266.0 Wednesday
969 2023-05-18 31715 21872.714286 12581.642857 20339.254795 65.10 350.0 Thursday
970 2023-05-19 32481 23309.142857 13741.678571 20426.805479 67.10 330.0 Friday
971 2023-05-20 17741 24619.714286 14307.750000 20471.232877 37.20 629.0 Saturday
972 2023-05-21 9439 25968.142857 14623.892857 20486.112329 25.20 749.0 Sunday
973 2023-05-22 28640 27049.428571 15642.571429 20541.139726 59.70 404.0 Monday
974 2023-05-23 34138 27193.714286 16761.500000 20579.046575 69.30 308.0 Tuesday
975 2023-05-24 16854 24429.714286 17351.535714 20552.175342 35.70 644.0 Wednesday
976 2023-05-25 41420 25816.142857 18816.607143 20591.695890 81.70 184.0 Thursday
977 2023-05-26 25947 24882.714286 19651.750000 20492.838356 53.10 470.0 Friday
978 2023-05-27 16401 24691.285714 20234.428571 20441.893151 34.90 652.0 Saturday
979 2023-05-28 0 23342.857143 20208.428571 20404.309589 2.85 972.5 Sunday
980 2023-05-29 1259 19431.285714 19574.285714 20382.767123 11.60 885.0 Monday
981 2023-05-30 33991 19410.285714 19557.642857 20411.580822 68.80 313.0 Tuesday
982 2023-05-31 64683 26243.000000 21224.750000 20534.572603 98.30 18.0 Wednesday
983 2023-06-01 29651 24561.714286 20939.785714 20580.375342 61.50 386.0 Thursday
984 2023-06-02 35650 25947.857143 21542.964286 20603.668493 72.40 277.0 Friday
985 2023-06-03 7364 24656.857143 21799.928571 20558.572603 22.30 778.0 Saturday
986 2023-06-04 29370 28852.571429 22828.857143 20622.049315 61.10 390.0 Sunday
987 2023-06-05 26859 32509.714286 23103.714286 20653.106849 56.10 440.0 Monday
988 2023-06-06 28010 31655.285714 23794.071429 20690.975342 58.40 417.0 Tuesday
989 2023-06-07 29368 26610.285714 24426.821429 20656.701370 61.00 391.0 Wednesday
990 2023-06-08 26378 26142.714286 24598.321429 20701.928767 54.70 454.0 Thursday
991 2023-06-09 1999 21335.428571 23868.785714 20635.723288 13.30 868.0 Friday
992 2023-06-10 0 20283.428571 23562.821429 20623.246575 2.85 972.5 Saturday
993 2023-06-11 14647 18180.142857 24085.928571 20663.087671 32.20 679.0 Sunday
994 2023-06-12 31281 18811.857143 24450.571429 20748.789041 64.20 359.0 Monday
995 2023-06-13 23745 18202.571429 24115.464286 20799.172603 48.90 512.0 Tuesday
996 2023-06-14 50339 21198.428571 24620.357143 20934.254795 91.80 83.0 Wednesday
997 2023-06-15 45835 23978.000000 25124.642857 21042.520548 87.40 127.0 Thursday
998 2023-06-16 40815 29523.142857 25422.285714 21048.158904 80.70 194.0 Friday
999 2023-06-17 12570 31318.857143 25237.607143 21033.315068 29.10 710.0 Saturday

The following cell outputs various keypress statistics. When this script is run hourly, these statistics (along with other ones) will then appear in the console terminal.

In [ ]:
days_with_data = len(df_combined_daily_keypresses)
#  The following column cell shows the ranks immediately above the ranks for the most recent day.
keypresses_today = df_combined_daily_keypresses.iloc[-1]['Keypresses']
percentile_today = df_combined_daily_keypresses.iloc[-1]['percentile']
rank_today = df_combined_daily_keypresses.iloc[-1]['rank']
print("Ranks are out of", days_with_data, "days.")
print(f"Today's keypresses: {keypresses_today}")
print(f"Your keypress totals yesterday and 7, 28, and 365 days ago were \
{df_combined_daily_keypresses.iloc[-2]['Keypresses']}, \
{df_combined_daily_keypresses.iloc[-8]['Keypresses']}, \
{df_combined_daily_keypresses.iloc[-29]['Keypresses']}, \
and {df_combined_daily_keypresses.iloc[-366]['Keypresses']}, respectively.")
# If your keypresses today are higher than these values, the moving averages
# associated with those values will increase.
print(f"Today's percentile: {round(percentile_today, 3)}")
print(f"Today's rank: {rank_today} \
(in front of {days_with_data - rank_today} days)")

Ranks are out of 1000 days.
Today's keypresses: 12570
Your keypress totals yesterday and 7, 28, and 365 days ago were 40815, 0, 17741, and 17988, respectively.
Today's percentile: 29.1
Today's rank: 710.0 (in front of 290.0 days)

Days ranked just ahead of today (along with today's rank):

In [ ]:
df_days_with_higher_keypresses = df_combined_daily_keypresses.sort_values(
    'rank').query("rank <= @rank_today").tail(11)
keypress_difference_list = [df_days_with_higher_keypresses.iloc[i][
    'Keypresses'] - df_days_with_higher_keypresses.iloc[i+1][
        'Keypresses'] for i in range(len(df_days_with_higher_keypresses) -1 )]
keypress_difference_list.append(np.NaN)
df_days_with_higher_keypresses[
    'diff_from_following_day'] = keypress_difference_list
df_days_with_higher_keypresses[
    'diff_from_current_day'] = df_days_with_higher_keypresses[
        'Keypresses'] - df_days_with_higher_keypresses.iloc[-1]['Keypresses']
df_days_with_higher_keypresses
Out[ ]:
Date Keypresses 7_day_ma 28_day_ma 365_day_ma percentile rank Weekday diff_from_following_day diff_from_current_day
851 2023-01-20 12953 17091.571429 17403.571429 20840.136986 30.1 700.0 Friday 20.0 383
618 2022-06-01 12933 25152.000000 17662.892857 23916.726027 30.0 701.0 Wednesday 4.0 363
98 2020-12-28 12929 6217.000000 29041.071429 NaN 29.9 702.0 Monday 18.0 359
173 2021-03-13 12911 38597.142857 35099.107143 NaN 29.8 703.0 Saturday 46.0 341
693 2022-08-15 12865 16522.857143 22526.535714 22779.410959 29.7 704.0 Monday 35.0 295
160 2021-02-28 12830 33836.285714 38871.571429 NaN 29.6 705.0 Sunday 24.0 260
572 2022-04-16 12806 30994.857143 29207.714286 23930.865753 29.5 706.0 Saturday 82.0 236
214 2021-04-23 12724 31193.857143 35058.035714 NaN 29.4 707.0 Friday 123.0 154
762 2022-10-23 12601 22229.285714 20244.000000 20422.243836 29.3 708.0 Sunday 11.0 31
819 2022-12-19 12590 26520.000000 29879.928571 20578.797260 29.2 709.0 Monday 20.0 20
999 2023-06-17 12570 31318.857143 25237.607143 21033.315068 29.1 710.0 Saturday NaN 0

Looking for days with identical non-zero keypress totals:

In [ ]:
duplicated_keypress_dates = df_combined_daily_keypresses[
df_combined_daily_keypresses.duplicated(
subset = 'Keypresses', keep = False)].query('Keypresses > 0').sort_values(
'Keypresses', ascending = False)
len(duplicated_keypress_dates)
Out[ ]:
16
In [ ]:
duplicated_keypress_dates
Out[ ]:
Date Keypresses 7_day_ma 28_day_ma 365_day_ma percentile rank Weekday
32 2020-10-23 48908 41125.571429 37996.178571 NaN 90.35 97.5 Friday
183 2021-03-23 48908 36708.000000 32516.464286 NaN 90.35 97.5 Tuesday
3 2020-09-24 34698 NaN NaN NaN 70.55 295.5 Thursday
124 2021-01-23 34698 34726.428571 24214.178571 NaN 70.55 295.5 Saturday
318 2021-08-05 32184 29066.857143 29787.500000 NaN 66.35 337.5 Thursday
676 2022-07-29 32184 26384.571429 26299.000000 23180.561644 66.35 337.5 Friday
687 2022-08-09 26905 21784.571429 25372.785714 22972.580822 56.25 438.5 Tuesday
765 2022-10-26 26905 25745.714286 20627.357143 20359.476712 56.25 438.5 Wednesday
55 2020-11-15 26550 36325.285714 35269.428571 NaN 55.15 449.5 Sunday
645 2022-06-28 26550 12407.857143 12670.821429 23373.895890 55.15 449.5 Tuesday
662 2022-07-15 25084 28583.714286 18304.428571 23319.049315 51.45 486.5 Friday
841 2023-01-10 25084 16131.571429 16824.607143 21120.632877 51.45 486.5 Tuesday
321 2021-08-08 6318 30379.714286 30359.035714 NaN 20.35 797.5 Sunday
632 2022-06-15 6318 6218.571429 15957.071429 23702.145205 20.35 797.5 Wednesday
536 2022-03-11 149 10365.285714 15863.214286 25284.586301 7.05 930.5 Friday
538 2022-03-13 149 8908.285714 15542.392857 25160.463014 7.05 930.5 Sunday
In [ ]:
df_combined_daily_keypresses
Out[ ]:
Date Keypresses 7_day_ma 28_day_ma 365_day_ma percentile rank Weekday
0 2020-09-21 19081 NaN NaN NaN 40.30 598.0 Monday
1 2020-09-22 32771 NaN NaN NaN 67.50 326.0 Tuesday
2 2020-09-23 32065 NaN NaN NaN 65.90 342.0 Wednesday
3 2020-09-24 34698 NaN NaN NaN 70.55 295.5 Thursday
4 2020-09-25 47038 NaN NaN NaN 88.50 116.0 Friday
... ... ... ... ... ... ... ... ...
995 2023-06-13 23745 18202.571429 24115.464286 20799.172603 48.90 512.0 Tuesday
996 2023-06-14 50339 21198.428571 24620.357143 20934.254795 91.80 83.0 Wednesday
997 2023-06-15 45835 23978.000000 25124.642857 21042.520548 87.40 127.0 Thursday
998 2023-06-16 40815 29523.142857 25422.285714 21048.158904 80.70 194.0 Friday
999 2023-06-17 12570 31318.857143 25237.607143 21033.315068 29.10 710.0 Saturday

1000 rows × 8 columns

In [ ]:
df_combined_daily_keypresses['Date']
Out[ ]:
0     2020-09-21
1     2020-09-22
2     2020-09-23
3     2020-09-24
4     2020-09-25
         ...    
995   2023-06-13
996   2023-06-14
997   2023-06-15
998   2023-06-16
999   2023-06-17
Name: Date, Length: 1000, dtype: datetime64[ns]

Plotting Chronological Keypress Data¶

Now that we have a DataFrame showing daily keypresses and multiple moving averages, it's time to visualize it! The advantage of creating this chart within Plotly is that, being HTML-based, it is interactive in nature. Thus, you can hover over the lines to view the values corresponding to those lines and zoom in to get a closer look at a particular section of the graph. As before, though, this graph can also be saved as a static image.

In [ ]:
fig_keypresses_line_chart = px.line(df_combined_daily_keypresses, x = 'Date', 
y = ['Keypresses', '7_day_ma', '28_day_ma', '365_day_ma']) 
# Note that multiple y values can be passed to the line chart. 

save_chart(fig_keypresses_line_chart, 'px_daily_keypresses_and_mas')

fig_keypresses_line_chart
In [ ]:
Image(static_graphs_folder+'px_daily_keypresses_and_mas.png')
Out[ ]:

Monthly keypress totals:¶

In [ ]:
df_monthly_keypresses = df_combined_daily_keypresses.copy().set_index(
    'Date').resample('M').sum()['Keypresses'].reset_index()
df_monthly_keypresses['Month'] = df_monthly_keypresses['Date'].dt.to_period('M')
df_monthly_keypresses['Year'] = df_monthly_keypresses['Date'].dt.to_period('Y')
df_monthly_keypresses.drop('Date', axis = 1, inplace = True)
df_monthly_keypresses['Keypresses'] = df_monthly_keypresses.pop('Keypresses')
df_monthly_keypresses
Out[ ]:
Month Year Keypresses
0 2020-09 2020 346918
1 2020-10 2020 1140421
2 2020-11 2020 1061614
3 2020-12 2020 823763
4 2021-01 2021 980116
5 2021-02 2021 1088404
6 2021-03 2021 1142850
7 2021-04 2021 833464
8 2021-05 2021 700155
9 2021-06 2021 621894
10 2021-07 2021 881137
11 2021-08 2021 722970
12 2021-09 2021 919571
13 2021-10 2021 1051663
14 2021-11 2021 797115
15 2021-12 2021 515936
16 2022-01 2022 729233
17 2022-02 2022 487910
18 2022-03 2022 552559
19 2022-04 2022 834250
20 2022-05 2022 627263
21 2022-06 2022 412725
22 2022-07 2022 758759
23 2022-08 2022 691636
24 2022-09 2022 330566
25 2022-10 2022 706081
26 2022-11 2022 756509
27 2022-12 2022 720251
28 2023-01 2023 652155
29 2023-02 2023 580208
30 2023-03 2023 892786
31 2023-04 2023 327674
32 2023-05 2023 665769
33 2023-06 2023 433881

Saving the updated version of this DataFrame to a .csv file:¶

In [ ]:
df_combined_daily_keypresses.to_csv(
    'data/df_combined_daily_keypresses_updated.csv')

Hourly keypress stats:¶

In order to calculate hourly keypress statistics, we'll need to create a new DataFrame that aggregates keypresses by hour instead of by day.

In [ ]:
hourly_keypress_db_list = []

for db_path in database_paths_list:
    hourly_keypress_db_list.append(generate_keypress_totals(
        database_path = db_path, level = 'hourly'))

df_hourly_keypresses = pd.concat([
    df for df in hourly_keypress_db_list]).reset_index(drop=True)

# As with my daily keypresess DataFrame, I'll use pivot_table() to group 
# multiple rows for the same day and hour into a single row. (These multiple 
# rows are the result of my using multiple computers during the same hour.)
df_hourly_keypresses = df_hourly_keypresses.pivot_table(index = [
    'Day', 'Hour'], values = 'Keypresses', aggfunc = 'sum').reset_index().sort_values(['Day', 'Hour'])

df_hourly_keypresses.to_csv(
    'data/df_combined_hourly_keypresses.csv', index = False)
df_hourly_keypresses
Out[ ]:
Day Hour Keypresses
0 2020-09-21 15 278
1 2020-09-21 16 567
2 2020-09-21 17 1218
3 2020-09-21 18 3300
4 2020-09-21 19 3888
... ... ... ...
11394 2023-06-17 12 26
11395 2023-06-17 13 0
11396 2023-06-17 14 4274
11397 2023-06-17 15 0
11398 2023-06-17 16 1748

11399 rows × 3 columns

Recreating the DataFrame from a .csv file so that the following cells can be run by users who don't yet have their own WhatPulse database:

In [ ]:
df_hourly_keypresses = pd.read_csv('data/df_combined_hourly_keypresses.csv')
In [ ]:
df_hourly_keypresses['Day'] = pd.to_datetime(df_hourly_keypresses['Day'])
# Creating a combined day/hour column:
df_hourly_keypresses['Day_and_Hour'] = df_hourly_keypresses[
    'Day'] + pd.to_timedelta(df_hourly_keypresses['Hour'], unit = 'H')
df_hourly_keypresses.set_index('Day_and_Hour', inplace = True)
df_hourly_keypresses
Out[ ]:
Day Hour Keypresses
Day_and_Hour
2020-09-21 15:00:00 2020-09-21 15 278
2020-09-21 16:00:00 2020-09-21 16 567
2020-09-21 17:00:00 2020-09-21 17 1218
2020-09-21 18:00:00 2020-09-21 18 3300
2020-09-21 19:00:00 2020-09-21 19 3888
... ... ... ...
2023-06-17 12:00:00 2023-06-17 12 26
2023-06-17 13:00:00 2023-06-17 13 0
2023-06-17 14:00:00 2023-06-17 14 4274
2023-06-17 15:00:00 2023-06-17 15 0
2023-06-17 16:00:00 2023-06-17 16 1748

11399 rows × 3 columns

The following cells add in hours with 0 keypresses (of which there are many!). In order to include current/previous hours for the current day in my results, I'll add in keypresses up to the start of the next day (i.e. midnight), then limit the results so that they don't extend beyond the current hour.

Calculating tomorrow's date:

In [ ]:
last_date_for_hourly_keypress_log = last_date + datetime.timedelta(days = 1)
last_date_for_hourly_keypress_log
Out[ ]:
Timestamp('2023-06-18 00:00:00')
In [ ]:
pd.Timestamp.now()
Out[ ]:
Timestamp('2023-06-17 16:46:56.400190')

Adding hours without keypresses to the DataFrame:

In [ ]:
full_hourly_date_range = pd.date_range(start = first_date, 
end = last_date_for_hourly_keypress_log, freq = 'H')
df_hourly_keypresses = df_hourly_keypresses.reindex(full_hourly_date_range)
df_hourly_keypresses['Keypresses'].fillna(0, inplace = True)
df_hourly_keypresses['Keypresses'] = df_hourly_keypresses[
    'Keypresses'].astype('int')
# Retrieving date and hour values from the index:
df_hourly_keypresses['Day'] = df_hourly_keypresses.index.date
df_hourly_keypresses['Hour'] = df_hourly_keypresses.index.hour
df_hourly_keypresses
Out[ ]:
Day Hour Keypresses
2020-09-21 00:00:00 2020-09-21 0 0
2020-09-21 01:00:00 2020-09-21 1 0
2020-09-21 02:00:00 2020-09-21 2 0
2020-09-21 03:00:00 2020-09-21 3 0
2020-09-21 04:00:00 2020-09-21 4 0
... ... ... ...
2023-06-17 20:00:00 2023-06-17 20 0
2023-06-17 21:00:00 2023-06-17 21 0
2023-06-17 22:00:00 2023-06-17 22 0
2023-06-17 23:00:00 2023-06-17 23 0
2023-06-18 00:00:00 2023-06-18 0 0

24001 rows × 3 columns

Limiting the results to the period leading up to the current hour:

In [ ]:
df_hourly_keypresses = df_hourly_keypresses[
    df_hourly_keypresses.index < pd.Timestamp.now()].copy()
df_hourly_keypresses.reset_index(drop=True,inplace=True)

Calculating rolling 24-hour keypress totals:

In [ ]:
df_hourly_keypresses['keypresses_over_last_24_hours'] = df_hourly_keypresses[
    'Keypresses'].rolling(24).sum()
df_hourly_keypresses
Out[ ]:
Day Hour Keypresses keypresses_over_last_24_hours
0 2020-09-21 0 0 NaN
1 2020-09-21 1 0 NaN
2 2020-09-21 2 0 NaN
3 2020-09-21 3 0 NaN
4 2020-09-21 4 0 NaN
... ... ... ... ...
23988 2023-06-17 12 26 41414.0
23989 2023-06-17 13 0 40460.0
23990 2023-06-17 14 4274 39039.0
23991 2023-06-17 15 0 36194.0
23992 2023-06-17 16 1748 34831.0

23993 rows × 4 columns

Printing out recent hours with keypresses: (This data will also appear on the terminal window when the program is run automatically, which allows you to track your recent productivity.)

In [ ]:
print("Keypresses over the last 25 hours (excluding hours \
with 0 keypresses):\n",df_hourly_keypresses.iloc[-25:].query("Keypresses > 0"))
# Hours with 0 keypresses are removed in order to give the console output more
# space to fit on a single line.

Keypresses over the last 25 hours (excluding hours with 0 keypresses):
               Day  Hour  Keypresses  keypresses_over_last_24_hours
23968  2023-06-16    16        3111                        36370.0
23969  2023-06-16    17        5911                        37877.0
23970  2023-06-16    18        4244                        42121.0
23971  2023-06-16    19        1453                        39374.0
23972  2023-06-16    20        4405                        42592.0
23973  2023-06-16    21        2910                        44398.0
23974  2023-06-16    22        2719                        42621.0
23975  2023-06-16    23         619                        40815.0
23985  2023-06-17     9          16                        40101.0
23986  2023-06-17    10         927                        38011.0
23987  2023-06-17    11        5579                        41388.0
23988  2023-06-17    12          26                        41414.0
23990  2023-06-17    14        4274                        39039.0
23992  2023-06-17    16        1748                        34831.0

Keypresses for the last 48 hours (including hours with 0 keypresses, now that they have been added to our table):

In [ ]:
df_hourly_keypresses.iloc[-48:]
Out[ ]:
Day Hour Keypresses keypresses_over_last_24_hours
23945 2023-06-15 17 4404 55646.0
23946 2023-06-15 18 0 48818.0
23947 2023-06-15 19 4200 43512.0
23948 2023-06-15 20 1187 44163.0
23949 2023-06-15 21 1104 45267.0
23950 2023-06-15 22 4496 44316.0
23951 2023-06-15 23 2425 45835.0
23952 2023-06-16 0 104 45939.0
23953 2023-06-16 1 0 45939.0
23954 2023-06-16 2 0 45939.0
23955 2023-06-16 3 0 45939.0
23956 2023-06-16 4 0 45939.0
23957 2023-06-16 5 0 45939.0
23958 2023-06-16 6 0 45939.0
23959 2023-06-16 7 0 45939.0
23960 2023-06-16 8 0 45939.0
23961 2023-06-16 9 626 45774.0
23962 2023-06-16 10 3017 46448.0
23963 2023-06-16 11 2202 43874.0
23964 2023-06-16 12 0 39513.0
23965 2023-06-16 13 954 36085.0
23966 2023-06-16 14 5695 38748.0
23967 2023-06-16 15 2845 37453.0
23968 2023-06-16 16 3111 36370.0
23969 2023-06-16 17 5911 37877.0
23970 2023-06-16 18 4244 42121.0
23971 2023-06-16 19 1453 39374.0
23972 2023-06-16 20 4405 42592.0
23973 2023-06-16 21 2910 44398.0
23974 2023-06-16 22 2719 42621.0
23975 2023-06-16 23 619 40815.0
23976 2023-06-17 0 0 40711.0
23977 2023-06-17 1 0 40711.0
23978 2023-06-17 2 0 40711.0
23979 2023-06-17 3 0 40711.0
23980 2023-06-17 4 0 40711.0
23981 2023-06-17 5 0 40711.0
23982 2023-06-17 6 0 40711.0
23983 2023-06-17 7 0 40711.0
23984 2023-06-17 8 0 40711.0
23985 2023-06-17 9 16 40101.0
23986 2023-06-17 10 927 38011.0
23987 2023-06-17 11 5579 41388.0
23988 2023-06-17 12 26 41414.0
23989 2023-06-17 13 0 40460.0
23990 2023-06-17 14 4274 39039.0
23991 2023-06-17 15 0 36194.0
23992 2023-06-17 16 1748 34831.0

Making sure that all rows with the same day and hour (e.g. from multiple WhatPulse databases) have been merged into the same row:

In [ ]:
df_hourly_keypresses[df_hourly_keypresses.duplicated(
    subset = ['Day', 'Hour'], keep = False)]
Out[ ]:
Day Hour Keypresses keypresses_over_last_24_hours

Most keypresses typed in a single hour within the entire dataset:

In [ ]:
df_hourly_keypresses.sort_values('Keypresses', ascending = False).head(50)
Out[ ]:
Day Hour Keypresses keypresses_over_last_24_hours
19342 2022-12-05 22 17552 83422.0
8650 2021-09-16 10 15446 64271.0
1958 2020-12-11 14 14618 49483.0
13290 2022-03-28 18 13406 44448.0
9442 2021-10-19 10 13345 63492.0
7907 2021-08-16 11 12960 24082.0
3378 2021-02-08 18 12777 53030.0
18190 2022-10-18 22 12173 30179.0
6023 2021-05-29 23 12045 18290.0
6912 2021-07-06 0 11999 26316.0
18062 2022-10-13 14 11698 40281.0
18061 2022-10-13 13 11634 31604.0
13359 2022-03-31 15 11554 50621.0
3210 2021-02-01 18 11331 62572.0
759 2020-10-22 15 11301 48012.0
2663 2021-01-09 23 11300 30575.0
6793 2021-07-01 1 11289 35355.0
8986 2021-09-30 10 11119 66954.0
3548 2021-02-15 20 11113 74179.0
22366 2023-04-10 22 11057 49524.0
10789 2021-12-14 13 11011 35159.0
1119 2020-11-06 15 10981 51438.0
2607 2021-01-07 15 10759 35737.0
21686 2023-03-13 14 10758 37979.0
7223 2021-07-18 23 10732 29008.0
252 2020-10-01 12 10716 48142.0
8985 2021-09-30 9 10702 60848.0
7391 2021-07-25 23 10576 25075.0
19127 2022-11-26 23 10565 49127.0
2629 2021-01-08 13 10517 56691.0
3710 2021-02-22 14 10501 50932.0
10863 2021-12-17 15 10468 31397.0
897 2020-10-28 9 10340 67613.0
22148 2023-04-01 20 10283 18918.0
14174 2022-05-04 14 10218 62136.0
9441 2021-10-19 9 10176 52756.0
4600 2021-03-31 16 10055 59622.0
10506 2021-12-02 18 10043 46470.0
447 2020-10-09 15 10023 55454.0
4313 2021-03-19 17 10021 48519.0
9658 2021-10-28 10 10013 61658.0
9322 2021-10-14 10 10000 66127.0
108 2020-09-25 12 9953 48047.0
7487 2021-07-29 23 9948 48558.0
10790 2021-12-14 14 9886 42935.0
17807 2022-10-02 23 9872 10006.0
4959 2021-04-15 15 9841 32667.0
3709 2021-02-22 13 9813 40431.0
18068 2022-10-13 20 9788 44976.0
5950 2021-05-26 22 9783 36905.0

Average keypresses by hour:

In [ ]:
df_hourly_pivot = df_hourly_keypresses.pivot_table(index = 'Hour', 
values = 'Keypresses', aggfunc = ['mean', 'sum']).reset_index()
df_hourly_pivot.columns = df_hourly_pivot.columns.to_flat_index()
# At this point, the columns will read: "(hour, ),	(mean, Keypresses), and
# (sum, Keypresses)". We could convert them to regular columns via a loop,
# but since there are only 3, the simplest solution is to simply rename them
# as follows:
df_hourly_pivot.columns = ['Hour', 'Average Keypresses', 'Keypresses']

# Determining the percentage of total keypresses typed each hour:
total_keypresses = df_hourly_pivot['Keypresses'].sum()
df_hourly_pivot['pct_of_total'] = 100* df_hourly_pivot[
    'Keypresses'] / total_keypresses
df_hourly_pivot


df_hourly_pivot
Out[ ]:
Hour Average Keypresses Keypresses pct_of_total
0 0 999.012000 999012 4.030191
1 1 530.563000 530563 2.140385
2 2 200.571000 200571 0.809139
3 3 69.630000 69630 0.280900
4 4 13.642000 13642 0.055034
5 5 1.632000 1632 0.006584
6 6 0.338000 338 0.001364
7 7 2.765000 2765 0.011154
8 8 153.405000 153405 0.618863
9 9 613.537000 613537 2.475117
10 10 1271.721000 1271721 5.130347
11 11 1584.240000 1584240 6.391104
12 12 1545.269000 1545269 6.233888
13 13 1798.316000 1798316 7.254724
14 14 2147.468000 2147468 8.663265
15 15 2184.511000 2184511 8.812703
16 16 2218.676000 2218676 8.950531
17 17 1714.472472 1712758 6.909568
18 18 1354.026026 1352672 5.456918
19 19 1055.845846 1054790 4.255209
20 20 1033.404404 1032371 4.164767
21 21 1149.985986 1148836 4.634607
22 22 1553.336336 1551783 6.260167
23 23 1601.301301 1599700 6.453472
In [ ]:
sum(df_hourly_pivot['pct_of_total']) 
# Making sure the percentages were calculated correctly 
# (they should add up to 100%)
Out[ ]:
100.0

Plotting average keypresses by hour:¶

Not surprisingly, my hourly keypress averages are highest during the workday and lowest in the middle of the night, although I've been pretty active in the late evening hours also. (More on this below.)

In [ ]:
fig_hourly_keypresses = px.bar(df_hourly_pivot, x = 'Hour', 
y = 'Average Keypresses', text_auto = '.0f', 
title = 'Average Keypresses per Hour')
save_chart(fig_hourly_keypresses, 'average_keypresses_by_hour')
fig_hourly_keypresses
In [ ]:
Image(static_graphs_folder+'average_keypresses_by_hour.png')
Out[ ]:
In [ ]:
df_hourly_keypresses['Day'][0]
Out[ ]:
datetime.date(2020, 9, 21)

Saving the updated version of df_hourly_keypresses to a .csv file:

In [ ]:
df_hourly_keypresses.to_csv('data/df_combined_hourly_keypresses_updated.csv')

Data analysis question: Did marriage change my typing patterns?¶

I got married in April 2023, and I suspected that my keypress distributions as a married man might skew earlier than they did when I was a bachelor. I decided to investigate this graphically by creating subsets of df_hourly_keypresses that contained pre-marriage and post-marriage datasets, then comparing them via a grouped bar chart.

Setting datetime.date() values that will be used to filter df_hourly_keypresses:

In [ ]:
post_mba_work_start_date = datetime.date(2022, 6, 21) # I began my current
# full-time work in June 2022 after finishing my MBA. I chose to limit the
# dataset to this date range so that my results wouldn't be influenced
# by my time as an MBA student (which featured more irregular computer hours).
last_day_before_marriage = datetime.date(2023, 4, 14)
day_after_honeymoon = datetime.date(2023, 4, 29) # I didn't type much at all
# on my honeymoon, so I excluded this period from my analysis in order not
# to skew the average keypress totals downward.
In [ ]:
df_hourly_keypresses_pre_marriage = df_hourly_keypresses.query(
"Day > @post_mba_work_start_date & Day <= @last_day_before_marriage"
).pivot_table(index = 'Hour', values = 'Keypresses', aggfunc = 'mean').reset_index()
df_hourly_keypresses_pre_marriage['Period'] = 'Before Marriage'

df_hourly_keypresses_post_marriage = df_hourly_keypresses.query(
    "Day >= @day_after_honeymoon").pivot_table(
        index = 'Hour', values = 'Keypresses', aggfunc = 'mean').reset_index()
df_hourly_keypresses_post_marriage['Period'] = 'After Marriage'

# Combining these two DataFrames together:
df_hourly_keypresses_by_period = pd.concat([df_hourly_keypresses_pre_marriage,
df_hourly_keypresses_post_marriage])

df_hourly_keypresses_by_period
Out[ ]:
Hour Keypresses Period
0 0 641.848485 Before Marriage
1 1 296.060606 Before Marriage
2 2 58.700337 Before Marriage
3 3 31.047138 Before Marriage
4 4 0.090909 Before Marriage
5 5 0.000000 Before Marriage
6 6 1.138047 Before Marriage
7 7 4.380471 Before Marriage
8 8 62.777778 Before Marriage
9 9 574.838384 Before Marriage
10 10 1439.505051 Before Marriage
11 11 1632.949495 Before Marriage
12 12 1639.750842 Before Marriage
13 13 1856.245791 Before Marriage
14 14 1962.851852 Before Marriage
15 15 2019.952862 Before Marriage
16 16 1950.003367 Before Marriage
17 17 1412.973064 Before Marriage
18 18 941.222222 Before Marriage
19 19 888.138047 Before Marriage
20 20 873.659933 Before Marriage
21 21 1083.676768 Before Marriage
22 22 1323.804714 Before Marriage
23 23 1364.552189 Before Marriage
0 0 235.180000 After Marriage
1 1 0.000000 After Marriage
2 2 0.000000 After Marriage
3 3 0.000000 After Marriage
4 4 0.000000 After Marriage
5 5 0.000000 After Marriage
6 6 0.000000 After Marriage
7 7 0.660000 After Marriage
8 8 59.520000 After Marriage
9 9 689.000000 After Marriage
10 10 1892.540000 After Marriage
11 11 2157.920000 After Marriage
12 12 1735.240000 After Marriage
13 13 1992.300000 After Marriage
14 14 2652.420000 After Marriage
15 15 2248.760000 After Marriage
16 16 1956.040000 After Marriage
17 17 1497.408163 After Marriage
18 18 854.836735 After Marriage
19 19 1122.897959 After Marriage
20 20 775.183673 After Marriage
21 21 766.693878 After Marriage
22 22 933.836735 After Marriage
23 23 569.244898 After Marriage

My daily keypress counts have increased slightly since getting married (at least when the honeymoon isn't taken into account):

In [ ]:
df_hourly_keypresses_by_period.pivot_table(
    index = 'Period', values = 'Keypresses', aggfunc = 'sum')
Out[ ]:
Keypresses
Period
After Marriage 22139.682041
Before Marriage 22060.168350

However, as the following chart shows, the hourly distribution of these keypresses has changed significantly. I'm now typing much less late at night and am getting more keypresses in earlier in the day.

In [ ]:
fig_keypresses_by_period = px.bar(df_hourly_keypresses_by_period, x = 'Hour', 
y = 'Keypresses', color = 'Period', barmode = 'group', text_auto = '.0f', 
title = 'Average Keypresses by Hour Before and After Getting Married')
save_chart(fig_keypresses_by_period, 'keypresses_before_and_after_marriage')
# See https://plotly.com/python/bar-charts/ 
# for the use of the 'color' and 'barmode' arguments.
fig_keypresses_by_period
In [ ]:
Image(static_graphs_folder+'keypresses_before_and_after_marriage.png')
Out[ ]:
In [ ]:
end_time = time.time()
run_time = end_time - start_time
run_minutes = run_time // 60
run_seconds = run_time % 60

run_minutes, run_seconds
Out[ ]:
(0.0, 8.203047275543213)

The input() function within the following cell keeps the console window open when running the file in a command prompt. It's not necessary for the Jupyter Notebook, but when I export this notebook as a Python script and then run the script on a scheduled basis, this line gives me time to read the output. See nosklo's response at: https://stackoverflow.com/a/1000968/13097194

In [ ]:
print("The program has finished running. Press Enter to exit.") # Lets me know
# that I can now close the program after it has finished running in a console
# window. (I wouldn't want to close it while the 
# graphs are in the process of being generated.)
input() 

The program has finished running. Press Enter to exit.
Out[ ]:
''

That's it for this program! I hope you enjoy using it to analyze your own WhatPulse keypress statistics.